Ongoing Research Projects
The study aims to reinforce self-regulation of students, especially strategic planning and metacognition, using a personalized study guide for each student. The study guide was generated through a fully-automated process to minimize the instructional cost. It was based on the performance of student's online quizzes combined with their confidence level indication of their answers to each question. The study was based on my past research evidence that the reason students struggle in CS is lacking self-regulation. The project is currently finishing up its second administration and publishing a paper with the initial run data at SIGCSE 2021.
Multi-Themed Labs (05/2020~)
The study aims to improve student engagement in a large course by enabling students to personalize their learning experience and to collaborate with peers. The lab content is a web-based pre-written form using Ed Lessons, which allowed students to work at their own pace and receive individual assistance when necessary. Moreover, I offered three sets of lab material for every lab, themed in arts, biology, and regular CS, so students can find the most engaging learning material for themselves based on their own motivation and interest in CS. During labs, students were also asked to follow pair-programming practice to facilitate additional engagement. The team is currently evaluating the effectiveness of multi-themed labs. You can find sample material here.
Developing AI4All Summer Camp Toolkit (02/2020~)
The project develops a comprehensive toolkit for AI4All summer camps for teens, which includes guidelines on student recruiting, admission criteria, summer camp curriculum, instructor training, and student evaluation method. The project currently sets up guidelines on student recruiting and admission process by compiling reflections of the past summer camps hosted in multiple institutions.
Developing AI Principles Curriculum (05/2020~)
Inspired by my collaboration with AI4All, my research team is currently developing a new introductory AI course that is geared towards non-CS majors. The course aims to highlight societal implications and ethics of AI, while introducing basic technical background of AI. The project was motivated by the survey results that underrepresented students with respect to race/ethnicity are ~6 times less likely to take a conventional introductory AI course. We aim to pilot the course in 2021 at Princeton University.
Effective Uses of Course Learning Objectives (05/2020~)
In pursuit of better classroom equity in CS1 at Princeton University, this project has so far crafted SOLO-taxonomy-based learning objectives and categorized each learning objective to be either required or bonus. By knowing exactly what is required and what is bonus, students were able to decide how much additional time and effort they like to spend on learning material beyond what is absolutely necessary for the course. The team is currently is investigating how we can teach students to utilize learning objectives more effectively.